Supervised machine learning for text analysis in R
Author(s)
Bibliographic Information
Supervised machine learning for text analysis in R
（Chapman & Hall/CRC data science series）
CRC Press, 2022
- : pbk
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Note
Includes bibliographical references (p. 369-378) and index
Description and Table of Contents
Description
How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive models?
Build beginning-to-end workflows for predictive modeling using text as features
Compare traditional machine learning methods and deep learning methods for text data
Table of Contents
1. Language and modeling. 2. Tokenization. 3. Stop words. 4. Stemming. 5. Word Embeddings. 6. Regression. 7. Classification. 8. Dense neural networks. 9. Long short-term memory (LSTM) networks. 10. Convolutional neural networks.
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